Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
LOGML: Log Markup Language for Web Usage Mining
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Mining adaptively frequent closed unlabeled rooted trees in data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Minimum description length principle: generators are preferable to closed patterns
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Mining frequent closed rooted trees
Machine Learning
CLAIM: an efficient method for relaxed frequent closed itemsets mining over stream data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
An output-polynomial time algorithm for mining frequent closed attribute trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Mining frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
XStreamCluster: an efficient algorithm for streaming XML data clustering
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Kernel-based selective ensemble learning for streams of trees
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A new method of mining data streams using harmony search
Journal of Intelligent Information Systems
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We propose a new method to classify patterns, using closed and maximal frequent patterns as features. Generally, classification requires a previous mapping from the patterns to classify to vectors of features, and frequent patterns have been used as features in the past. Closed patterns maintain the same information as frequent patterns using less space and maximal patterns maintain approximate information. We use them to reduce the number of classification features. We present a new framework for XML tree stream classification. For the first component of our classification framework, we use closed tree mining algorithms for evolving data streams. For the second component, we use state of the art classification methods for data streams. To the best of our knowledge this is the first work on tree classification in streaming data varying with time. We give a first experimental evaluation of the proposed classification method.